Scientists In Literature Research Articles

Brain Tumor Assortment into High Level and Low Level Gliomas

Abstract: Brain is recognized as one of the complex organ of the human body. Abnormal formation of cells may affect the normal functioning of the brain. These abnormal cells may belong to category of benign cells resulting in low grade glioma or malignant cells resulting in high grade glioma. The treatment plans vary according to grade of glioma detected. This results in need of precise glioma grading. As per World Health Organization, biopsy is considered to be gold standard in glioma grading. Biopsy is an invasive procedure which may contains sampling errors. Biopsy may also contain subjectivity errors. This motivated the clinician to look for other methods which may overcome the limitations of biopsy reports. Machine learning and deep learning approaches using MRI is considered to be most promising alternative approach reported by scientist in literature. The presented work were based on the concept of AdaBoost approach which is an ensemble learning approach. The developed model was optimized w.r.t to two hyper parameters i.e. no. of estimators and learning rate keeping the base model fixed. The decision tree was used as a base model. The proposed developed model was trained and validated on BraTS 2018 dataset. The developed optimized model achieves reasonable accuracy in carrying out classification task i.e. high grade glioma vs. low grade glioma. Keywords: High grade glioma, low grade glioma, AdaBoost, Texture Features,

Brain Tumor Classification into High Grade and Low Grade Gliomas

Brain is recognized as one of the complex organ of the human body. Abnormal formation of cells may affect the normal functioning of the brain. These abnormal cells may belong to category of benign cells resulting in low grade glioma or malignant cells resulting in high grade glioma. The treatment plans vary according to grade of glioma detected. This results in need of precise glioma grading. As per World Health Organization, biopsy is considered to be gold standard in glioma grading. Biopsy is an invasive procedure which may contains sampling errors. Biopsy may also contain subjectivity errors. This motivated the clinician to look for other methods which may overcome the limitations of biopsy reports. Machine learning and deep learning approaches using MRI is considered to be most promising alternative approach reported by scientist in literature. The presented work were based on the concept of AdaBoost approach which is an ensemble learning approach. The developed model was optimized w.r.t to two hyper parameters i.e. no. of estimators and learning rate keeping the base model fixed. The decision tree was us ed as a base model. The proposed developed model was trained and validated on BraTS 2018 dataset. The developed optimized model achieves reasonable accuracy in carrying out classification task i.e. high grade glioma vs. low grade glioma.

Where are the Scientists in Literature and Science?

Where are the Scientists in Literature and Science?

The Comparison of (n,p), (n,α), (n,2n) and (α,n) Reaction Cross-Sections for 7Li and 9Be Target Nuclei

The knowledge of the cross-section is important especially for the fusion and fission reactor technology. In the absence of experimental data or in the difficulty of measuring data, theoretical predictions of nuclear cross-section data are needed to be improved using model calculations or semi-empirical formulas and to be compared with model and formulas to obtain sensitive approaches. In this work, for 7Li and 9Be target nuclei used as blanket materials in fusion reactors, (n,p), (n,α), (n,2n) and (α,n) reaction cross-section data have been evaluated by using some available empirical and semi-empirical cross-section formulas. The (n,p), (n,α), (n,2n) and (α,n) reaction cross-section formulas developed for medium and heavy mass nuclei by some scientist in literature are applied to 7Li and 9Be light nuclei and then results obtained from any systematics are compared with each other and the existing experimental data.

The Scientist in Literature: Images and Stereotypes – Their Importance

The Scientist in Literature: Images and Stereotypes – Their Importance

The Scientist in Literature: Images and Stereotypes – Their Importance

Recent surveys of public attitudes to science and scientists have indicated that scientists are seen in highly stereotyped forms, most of them unfavourable. Before scientists can alter their public image, it is necessary for them to understand how it has arisen. One very important way is through the literary tradition – the manner in which scientists have been presented as fictional characters from the medieval alchemists to the computer experts and physicists of contemporary literature. These depictions of the scientist have not only reflected the writers' opinions of science and contemporary scientists, but have, in turn, influenced society's image of the scientist and the public response to science. There are five major stereotypes which recur with varying frequency throughout the history of Western literature: the evil alchemist; the stupid virtuoso or projector; the unfeeling researcher; the heroic adventurer, utopian or world saviour; the helpless discoverer unable to control his discovery. This review traces the emergence of these stereotypes, suggests why they arose and examines the effect they have had on contemporary attitudes to science.

Scientists in Literature and the Media: How Do They Rate?

The depiction of scientists in literature and film has both reflected and influenced popular attitudes to scientists and science. Today scientists are commonly regarded with a combination of fear, suspicion and blame. Examination of inherited stereotypes and the analyses of contemporary writers and film-makers suggests ways to improve scientists' public image.